AI search visibility optimization
AI-search optimization focuses on citations, entity signals, schema, structured content, AI Overviews, and visibility in ChatGPT, Perplexity, Gemini, and similar systems.
46.4%
Best tweets about Agentic SEO
28 Best Tweets About Agentic SEO (2026)
Explore the best tweets about agentic SEO, featuring autonomous research, content operations, technical audits, workflows, safeguards, and measurable results.
SEO agents and agentic workflows with clear tasks, tools, human oversight, safeguards, operating costs, limitations, and demonstrated outcomes.
- Creators
- 22
- Updated
Original Xholic analysis
What 28 top Agentic SEO posts reveal
The conversation presents agentic SEO as connected, data-driven workflows for research, production, diagnostics, and AI visibility. Posts emphasize structured inputs, live data, quality controls, and human judgment, while individual authors also report cost, speed, and traffic outcomes.
- Dominant tone
- Positive
- Median score
- 9.79
- Leading format
- Case Study
- Recent posts
- 39.3%
67.9% of posts
All-time engagement
35.7% of posts
Published in 90 days
Conversation map
The themes creators return to
Agentic web and agent-ready sites
The agentic web shifts optimization toward machine-readable, callable, reliable website capabilities, structured product data, and agent-friendly navigation and transactions.
28.6%
Content production and programmatic SEO
Multi-step content systems turn keyword and competitor data into briefs, drafts, programmatic landing pages, optimizations, publishing, refreshing, and indexing workflows.
28.6%
SEO research and diagnostics agents
Agents automate SEO research, audits, keyword discovery, SERP/competitor analysis, reporting, and Search Console diagnostics by connecting to live data sources and APIs.
28.6%
Agent workflow architecture
Persistent skills, MCP/API connectors, subagents, memory, schedules, queues, and data pipelines make repeatable SEO processes operational.
21.4%
Human oversight and quality control
Human review gates, QA checks, original expertise, strategic judgment, and safeguards are positioned as necessary controls for autonomous SEO work.
21.4%
Economics and demonstrated outcomes
Posts quantify faster execution, lower per-task costs, reduced tool spend, and traffic or click growth from agent-driven SEO systems.
17.9%
Real-time intent and opportunity detection
Real-time signals from trends, social platforms, news, support, and first-party data are used by agents to spot emerging demand beyond historical keyword volumes.
7.1%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 25
- Median reposts
- 3
- Median replies
- 5
- Median views
- 2.6K
Posts with media make up 60.7% of this collection. Their median all-time score is 18.7, compared with 4.79 for text-only posts.
Format mix
- Case Study 35.7% Β· score 14.3
- Opinion 32.1% Β· score 6.17
- Announcement 25% Β· score 8.94
- Tutorial 7.1% Β· score 161.6
Consensus and debate
Where creators agree, and where they do not
Shared view
Agents are workflow systems
Posts describe connected workflows in which persistent skills and live data support repeatable research, drafting, QA, publishing, dashboards, and action queues rather than isolated chat prompts.
Shared view
Visibility shifts toward machine-readable signals
Posts discuss a shift from conventional rankings toward AI visibility and agent usability, citing structured content and outputs, entity signals, markup, clear pricing, merchant information, and reliable callable interfaces as relevant inputs.
Shared view
Human judgment remains central
Several posts cast agents as execution accelerators while assigning people responsibility for strategy, customer understanding, link relationships, and review gates.
Open debate
How autonomous should SEO be?
Views on autonomy differ. One post argues AI can review better than many SEOs while also retaining a further review step; other posts call for human review gates and say AI cannot replace customer understanding, relationship building, or strategic pivots.
What performs
Patterns behind standout posts
Outliers cover both strategy and execution
The five supplied benchmark outliers cover research automation, an agentic-web thesis, content production, workflow architecture, and local-service-business operations. Their all-time scores range from 187.35 to 510.96.
Posts foreground cost and outcome claims
Individual posts make specific economics and outcome claims: deep GSC analysis at about $0.04β0.06 per query, a competitor-research workflow for 8 cents, personalized sales decks for under $3 per lead, and sites reaching 278, 74, and 28 daily clicks. These are author-reported claims, not independently validated results.
Tutorial posts pair concrete build steps with strong median performance
The two tutorial-format posts in the supplied analytics have a median all-time score of 161.59. Both offer detailed workflow steps, including tool connections, data retrieval, content generation, publishing, and refresh or scheduling processes.
Statistical standouts
- View standout post 1 Score 511.0 Β· 52.19Γ median
- View standout post 2 Score 393.2 Β· 40.16Γ median
- View standout post 3 Score 279.2 Β· 28.52Γ median
- View standout post 4 Score 216.3 Β· 22.09Γ median
- View standout post 5 Score 187.3 Β· 19.14Γ median
Creator landscape
Who shapes this conversation
The five most represented creators account for 35.7% of the selected posts.
-
1. Cody Schneider
@codyschneider
2 posts
-
2. Corey Haines
@coreyhainesco
2 posts
-
3. Jan-Willem Bobbink
@jbobbink
2 posts
-
4. Julian Goldie SEO
@JulianGoldieSEO
2 posts
-
5. Marie Haynes
@Marie_Haynes
2 posts
-
6. Semrush
@semrush
2 posts
Cody Schneider emphasizes operational pipelines
Cody Schneider describes an end-to-end production workflow involving competitor and SERP research, an original-perspective transcript, CMS publishing, content refreshes, recurring jobs, and data-pipeline safeguards.
Corey Haines packages repeatable skills
Corey Haines presents reusable agent skills for scalable page templates and AI-search optimization, including citability-oriented structure, entity signals, and technical markup.
Jan-Willem Bobbink prioritizes live signals
Jan-Willem Bobbink describes a Search Console diagnostic agent and advocates monitoring live signals such as trends, social conversations, news, helpdesk questions, surveys, and reviews alongside historical keyword data.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 28-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best Agentic SEO tweets
Top Agentic SEO tweets from 22 creators
Ranked 01β28
- 01
- 02
@andrewchen Β·
Web 1.0 came with new channels: - email, search, link sharing, etc Web 2.0 too: - feeds, creators, viral invites, etc Mobile: - app stores, SMS invites, vertical vid, mobile ads What about AI? Iβve been complaining that AI hasnβt come with much. But weβre seeing a big growth channel opening now: Products that are built as APIs/CLIs that can be pulled into new projects by Codex/Claude on the fly Maybe the βAI-native hotel appβ doesnβt mean a mobile booking app with an AI chat panel. It means a CLI that can book a hotel for you, that an AI agent can pull into a bespoke answer or project or into code. Bolting on an AI chat panel is this generationβs weak form of AI. Maybe the full reinvention involves making it agent-first not human-first and once you start looking at it that way, a lot of existing products suddenly feel mis-specified. theyβre built as destinations, but agents donβt want destinations. they want capabilities. composable, callable, reliable capabilities. So instead of βgo to Expediaβ or βopen the app,β the future interaction is more like: an agent assembles a workflow on the fly. it pulls a flight search tool, a hotel booking tool, maybe a weather model, maybe even your personal preference graph. none of these are full products in the traditional sense. theyβre more like endpoints with taste and state. This flips distribution completely. historically you win by owning the surface area. seo, app store ranking, homepage traffic. in an agent world, you win by being the default callable primitive. the thing that shows up again and again in agent-generated plans because it works, has clean interfaces, and returns structured outputs. distribution shifts from βtop of funnelβ to βtop of call stack.β And the crazy part is this might actually compress product surface area dramatically. the best products might look more like tight, extremely well-designed CLIs with opinionated defaults rather than sprawling UIs. almost like the stripe api moment, but for everything. imagine if every vertical had a βstripe-levelβ primitive that agents preferentially use. thereβs also a weird inversion of brand here. humans used to choose brands. now agents will. so the brand becomes partially machine-legible. reliability, latency, error rates, schema clarity. you can almost imagine βagent seoβ where the ranking factors are things like success rate across thousands of agent runs, or how easy your tool is to integrate in a chain-of-thought execution loop. This also suggests a new kind of moat. not just data or network effects, but integration depth with agent ecosystems. if claude or codex or openclaw learns that your tool is the safest way to accomplish X, it gets baked into prompts, templates, maybe even fine-tunes. you become a default. and defaults, historically, are insanely sticky. The contrarian take is that most current βAI featuresβ are a local maximum. chat panels, copilots, assistants. theyβre transitional. the real end state might look closer to invisible infrastructure that agents orchestrate. the ui is just a debug layer for humans to peek into what the agents are doing. so maybe the new growth channels for ai look like: - being callable - being composable - being reliable at scale in agent loops - being embedded in agent templates and workflows - being the default primitive in a given domain and if thatβs right, then the question for any new product isnβt βwhatβs the uiβ or even βwhatβs the killer feature.β itβs βwhatβs the minimal, highest-leverage capability we can expose such that agents will repeatedly choose us when building something new.β
- 03
@codyschneider Β·
you can just remix all your competitors website content in a weekend now with an SEO agent how find your 10 competitors find their sitemaps build a composite database of all their pages build a content map of what you should write about research what is ranking page one for target kewyords competitors content is targeting use data for seo API to find all this data include a 30 minute transcription of your opinion on the industry in the source material write the articles and landing pages publish all this content in one shot every month refresh the content based on content gap analysis, and the related search console data you're now competing with your competitors on SEO and AI search
- 04
@Charles_SEO Β·
What Claude skills are you using for SEO? Here's my FULL, custom built stack for reverse engineering SERPs to building the most detailed briefs you've ever seen... These aren't generic prompts, they're custom Skills I built specifically for how I do SEO, loaded into Claude as permanent tools (Connected to things like Ahrefs MCP) that run every time I need them: 1. SERP Consensus Analyser 2. Competitor Content Consensus 3. OnPage Optimisation 4. Competitor Backlink Analyser 5. Self-Audit QA Gate The key insight most people miss about Claude Skills: They're not prompts... They're persistent, reusable systems with specific methodologies baked in. Every skill has its own file with best practices, output formats, and decision logic with corresponding MCPs/Connectors. I built these over months of iteration! - SERP Consensus β Content Consensus β OnPage Optimization is a full content strategy pipeline. - Competitor Backlink Analyser feeds my link building campaigns. - Self-Audit QA Gate ensures quality control on everything. This is what I mean when I say AI makes good SEOs faster π It doesn't replace the strategy, it automates the execution of a strategy that took 17 years to develop. What skills are you running? Genuinely curious what other people have built already π
- 05
@boringmarketer Β·
I'm a partner in a "boring" local service business. here's what I've automated with AI: - call intelligence - branded social content - internal SOPs are now micro tools - review intel & replies - citation and directory listing opportunities - subcontractor pipeline - SEO obviously :) the agents work in the background, the results are in an easy to use dashboard & action queue the data from every source that matters can be viewed by the whole team, it plugs into every tool we use what I've learned is that most Main Street businesses DON'T WANT TO CHAT with AI, they just want the work done. the team is loving it. if you have a service business would love to know how you've applied AI and seen results...shoot me a message happy to share learnings or see if I can help...
- 06
@coreyhainesco Β·
I built a skill for Claude Code that creates SEO pages at scale using templates and data β programmatic SEO done right. You tell it the keyword pattern you want to target (like "[tool] alternative" or "best [category] software") and it designs the page template, maps out all the variations, projects traffic for each, and builds the content structure. Alternative pages, comparison pages, location pages, integration pages β it knows which formats work for which keyword patterns and how to make 150 pages that don't feel like thin content. One good programmatic SEO strategy can generate more organic traffic than a year of blog posts. It's called /programmatic-seo and it's part of Marketing Skills β a free, open source collection of 32 marketing skills for AI agents like Claude Code, Cursor, and Codex.
- 07
@codyschneider Β·
new yt vid live how to build your first AI agent for marketing you'll learn Here's what you'll learn from the video: How to build your first AI marketing agent that functions like a virtual employee you can delegate tasks to How to connect an agent to live business data (Google Search Console, Ahrefs, your CMS) so it makes decisions based on what actually drives revenue How to teach an agent a process step-by-step, the same way you'd onboard a human employee How to turn that taught process into a reusable skill the agent remembers How to run an automated keyword research workflow: pulling GSC + Ahrefs data, filtering by search intent, and rank-stacking opportunities How to check your CMS (via the Strapi API) to avoid writing about keywords you've already covered How to research what's currently ranking on page one using the Serper API, then extract that content with the Exa AI API to inform your draft How to layer in your own point of view (e.g., via a transcript) so posts blend SERP research with original perspective How to auto-publish finished posts back to your site through the Strapi API How to convert a one-off task into a recurring daily cron job the agent runs on its own Other marketing workflows you can hand off to agents: Facebook ads management (auto-pausing high CPM ads), Google Search Ads optimization toward a conversion event, social media scheduling and performance analysis, and full cold outbound (Apollo for emails, validation, Instantly for sending, reply handling) Why tying agents to a unified data pipeline and warehouse avoids the common failure modes of earlier agents: API rate limits, MCP failures, truncation, and context window bloat Why persistent memory + accurate live data is what makes this generation of agents actually capable of good decision-making watch full vid below
- 08
@coreyhainesco Β·
I built a skill that optimizes content for AI search engines β Google AI Overviews, ChatGPT, Perplexity, Gemini, and Claude. It audits your AI visibility across platforms, structures content for citability, builds entity authority signals, and implements the technical markup AI systems look for. Traditional SEO gets you ranked. AI SEO gets you cited. A well-structured page can get cited even from page 2 β AI systems select sources based on content quality and structure, not just rank position. It's called /ai-seo and it's part of Marketing Skills β a free, open source collection of 40 marketing skills for AI agents like Claude Code, Cursor, and Codex. npx skills add coreyhaines31/marketingskills
Watch video
- 09
@Marie_Haynes Β·
The traditional web of human browsing is ending and being replaced by the Agentic web. Google has outlined several new AI protocols that we need to understand including MCP, A2A and UCP. WebMCP will allow agents to use the functionality of your website without even rendering the pixels on the screen. In this article I share how Google is transforming Search into AI Search and why this is the biggest opportunity in SEO since the invention of the Search engine. https://t.co/4E1VXkvniI
- 10
@jbobbink Β·
I wanted to test why the AI in GSC is so useless. So I built a GSC agent with 16 subagents of my own. Weekend mornings are for gaming. Mine just happen to involve Google Search Console. I connected Google's Agent Development Kit and Gemini 2.5 to GSC and built what I call GSC Wizard. Instead of clicking through dashboards, you just ask it questions in plain English: "Why did we lose traffic last month?" or "Show me my top 20 keywords." It runs in two modes. Simple mode uses a single Gemini Flash agent. You get answers in 2 to 5 seconds for about $0.003 per query. Deep analysis mode is where it gets interesting. 9 specialist agents investigate your site in parallel. Regional traffic. Device splits. Brand vs non-brand. Keyword cannibalization. Striking distance opportunities. Bencmarking and Low-CTR pages. Content decay. Query decay. SEO experimentation measurements. Then a synthesis agent connects all the dots into one executive report with root causes, regional breakdowns, and a prioritized recovery plan. Under 15 seconds. Costs: ~$0.04β0.06 For the kind of analysis that used to take me a few hours in spreadsheets or Looker dashboards. The key difference from just dumping data into ChatGPT: the LLM never sees your raw data. The backend processes millions of rows server-side and sends compact summaries. Smart caching through Firestore means no redundant API calls. Estimated cost for personal use: $5 to $15 per month. I have never had this level of diagnostic power at my fingertips. Google gave us an AI chatbot that selects date ranges for you and nobody asked for. Maybe what we actually needed was AI that reads our own data and tells us what to fix. But to be fair, that would be an expensive tool. Open to feedback from fellow SEOs who want to use something like this. What questions would you ask your SEO agent wizard?
- 11
- 12
@illyism Β·
The new Agent A by @ahrefs is a pretty easy way to use the MCP / API I tried this prompt and it made a full PDF report π Let's do a blog SEO audit - grab our top pages filtered on /blog - for each top keyword, grab the volume x cpc to calculate potential max value and sort the most valuable 10 blog posts - for each of those blog posts, check our seo title, description, word count, etc - then grab the top 10 SERP for the keyword, and compare us against higher ranking blog posts and tell me how to improve Need to think of better prompts π€
- 13
- 14
@Hartdrawss Β·
we kicked off two $10,000+ client builds this week in spaces most agencies haven't touched yet. here's what the strategy and the architecture actually looked like. AEO pipeline for a US family office: Ahrefs flagged something recently that stopped me mid-scroll - websites with zero traditional SEO indexing are getting cited in AI search results. no backlinks. no domain authority. none of the signals that have mattered for the last decade. we're building directly into that gap. - two models, two jobs. Exa for competitor research, claude sonnet for articles. they don't talk to each other - two api calls stitched by a postgres review queue - couldn't return valid json when content is html - delimiters and regex extraction instead - cron fires daily, one article per call, human review gate at every stage - fully autonomous content in prod without a human gate = garbage indexed on google autonomous lead scoring and outreach agent for a B2B SaaS founder from Norway : most people don't realise twitter's algorithm isn't rule-based like every other platform. it runs on Grok. fully autonomous. that changes what you can reverse engineer from reply data entirely. - grok fast at temp 0.2 for ICP scoring. threshold at 6 to qualify - grok for context pull once the lead is qualified - full conversation history, signals, intent - llama-3.3-70b at temp 0.75 for DM generation using that context - low temp = consistent scoring. high temp = messages that don't all read the same - scores and messages render live over SSE while the stream runs both builds started on paper. not in a terminal. the most interesting decisions this week weren't about which models to pick. they were about where to keep the human in the loop and where not to.
- 15
@johncalhooon Β·
New workflow I've been running: 1. Claude researches my competitors β x402agency SEO Agent ($0.0025) 2. Reads their landing pages as markdown β x402agency Reader Agent ($0.003) 3. Finds keywords I'm missing β SEO Agent ($0.075) What it found: - Forbes: "Stripe, Visa, Mastercard Race To Build AI Agent Payment Rails" - Stripe raised $500M at $5B for agent payments - Visa launched a CLI for AI bot payments - "ai agent marketplace" = 880 searches/mo, $8.30 CPC Total: 8 cents. Ahrefs charges $99/mo for this. No logins. No API keys. Just Claude + micropayments. This is what "agentic" actually means... not chatbots with personality, but autonomous tools with wallets.
- 16
@aigleeson Β·
I FIRED MY SEO AGENCY AFTER FINDING THIS. It's called Claude SEO, a free Claude Code skill that runs a full site audit in 10-15 minutes. This got 25 sub-skills and 18 agents. All running in parallel across technical SEO, schema, and AI search readiness. > Every recommendation ships with a "how would we know this failed" check > Detects and generates Schema. org markup automatically > Scores pages for AI Overviews, not just classic search > Local SEO layer audits Google Business Profile and NAP consistency > Zero API keys needed to start Nothing leaves your machine. MIT License. 100% Opensource. https://t.co/56FvVPpn3o
- 17
@natmiletic Β·
Thinking about hiring an agency vs. automating SEO with AI? Here's what AI can do: β’ Write drafts β’ Suggest keywords β’ Speed up research Here's what it can't do: β’ Understand your actual customers β’ Build real relationships for links β’ Pivot strategy when needed Tools amplify talent. They don't replace it.
- 18
@jbobbink Β·
Your entire SEO strategy is based on outdated data. And your favorite keyword tool is the reason why. Every major data provider sells you the same thing: historical search volume. Averages based on months of old clicks and queries. Packaged in pretty graphs that make you feel like you know what's coming next. But you don't: you're looking in the rearview mirror while trying to navigate a highway that changes lanes every week. This is the shift most people miss. The tools we all rely on for keyword research are snapshots of where demand was. Not where it is right now. And definitely not where it's going. Think about it. Ahrefs, Semrush, Google Ads Keyword Planner, data4seo. They all pull from the same well: aggregated historical query data. Updated monthly at best. Sometimes quarterly. That works fine when search behavior moved slowly. It doesn't work when a single viral post, a breaking news story, or a new AI feature can reshape search demand overnight. So I changed my approach. I started building proactive agents that monitor live signals instead. Google Trends in real time. Social conversations on LinkedIn, Reddit, X. News cycles as they break. Comments and questions flooding into helpdesks and support tickets. That's where tomorrow's search volume lives today. Your customers are already telling you what they need. They're asking questions in your helpdesk. They're commenting on your social posts. They're filling out surveys and writing reviews. This is live intent data. Not 90-day-old averages. The SEO teams that will win in 2026 aren't the ones with the best keyword lists. They're the ones who build systems that listen to real-time demand signals and act on them before the competition even opens their keyword tool. You can even use it to automate internal linking. Historical data tells you what happened. Live data tells you what to do next. Stop planning your strategy with last quarter's numbers. Start building agents that predict the next wave before it shows up in Ahrefs.
- 19
@samuelthompson Β·
real world agent use building personalized sales decks that pull in prospects existing SEO/GEO data we go through last 12 month performance, current rankings, and technical audit with them live on their discovery call great way to make "our SEO isn't working" actually mean something to both our team and the lead then our team goes away and uses the fireflies transcript + this data to build personalized 90 day SEO roadmap total cost = < $3 / lead
- 20
@aaditsh Β·
Bots used to be junk traffic. Now they can buy things. Every company has spent money trying to block bots. Captchas, rate limits, fraud detection. For 20 years, the playbook was simple: block everything that isn't human. But AI agents can browse, compare, and buy things on behalf of real people. That makes bot traffic valuable for the first time ever. I keep thinking about this. Your website is designed for a human who decides in 2 seconds whether to stay. An AI agent doesn't care about your hero image or your brand colors. It cares about structured data, clear pricing, and whether your product actually matches what its user asked for (and probably other things about your reviews, ratings etc). SEO was built around ranking for a human searching Google. Soon it'll be about making sure an AI agent picks your product when it's shopping for someone. I don't think most companies are thinking about this yet.
- 21
@timothyjordan Β·
AI agents are becoming a primary way people discover and consume the web. When an agent visits your site, it needs to quickly find, read, and understand your pages. Sites that are easy for agents to navigate get cited more often, surface in more answers, and reach a wider audience. Here is a practical spec for AI-optimized websites here: https://t.co/gPgqp0Wqlv Worth implementing now, LMK what we should add.
- 22
@semrush Β·
You're already outsourcing decisions you used to make yourself. Think about the last time you asked ChatGPT or Gemini to find something for you. A tool, a restaurant, a recommendation for someone hard to buy for. You probably skimmed what it came back with, checked one or two links, and went with it. The AI agent did the research. You just approved. Now, platforms like Google, OpenAI, Microsoft, and Anthropic are building systems that go beyond surfacing information. AI can take action: book a table, start a trial, complete a purchase β on your behalf. This is the agentic web. And researchers have a name for the behavioral shift underneath it: the delegate economy. It's redefining how we drive brand visibility in 2026. https://t.co/i5jVt7CeRa.
- 23
@semrush Β·
Google announced new agentic capabilities coming to Search β including information agents that monitor the web on a user's behalf and Universal Cart that aggregates products from multiple retailers and services in one place. The bigger shift isnβt the feature set. Itβs where Search is heading next: delegated decision-making and transaction execution. Information agents introduce persistent, query-based monitoring. That changes the optimization model. Brands now need to compete not just for discovery, but for continuous AI evaluation as pricing, availability, relevance, and product signals evolve over time. Universal Cart pushes commerce further into aggregated, AI-curated experiences. Instead of competing through isolated storefronts, retailers increasingly compete inside recommendation layers controlled by Search itself. As Google expands agentic experiences, the inputs behind visibility become even more important: structured product data, accurate merchant information, trusted third-party signals, and consistent brand authority across the web. https://t.co/v5yobPZXxz.
- 24
@sharyph_ Β·
I Automated My 60-Minute Optimization Process to 60 Seconds Every week I spent over an hour optimizing blog posts: β Checking title lengths (under 60 chars) β Writing meta descriptions (155-160 chars exactly) β Creating URL slugs β Writing TL;DR summaries β Converting headings to questions β Adding answer capsules β Fixing hierarchy (H1βH2βH3) β Finding internal links β Writing alt text It was killing me. So I built an AI agent using Claude Code that does all of it. The process now: β Drop in my blog post β Run the agent β Get optimized content in 60 seconds Same quality. Zero manual work. This is what AI is actually for: eliminating tedious work you already know how to do. Not replacing your thinking. Automating your checklist. What repetitive task are you still doing manually that could be automated?
- 25
@JulianGoldieSEO Β·
This Gemini Update Changes EVERYTHING π€― Gemini just stopped being a chatbot. It's an AI agent now. β‘ The 2 updates that changed AI SEO: β Projects: A persistent context system. Gemini holds your files, instructions, and chat history per workflow. No more re-explaining your goals every session. Set it up once, it remembers forever. β Notebooks: All your references, files, and conversations in one place. Gemini pulls from all of it. The shift: chatbots respond. Agents act. Gemini now runs multi-step workflows across Docs, Gmail, Sheets, and 3rd-party tools end-to-end. π€ Real AI SEO use cases: β Competitor backlink analysis β set up the project once. Gemini scans 20 competitors weekly, ranks opportunities, delivers the report. Zero touch. β Content gap analysis β upload your data + competitor data. Gemini outputs a content calendar with target keywords mapped to article ideas. β Client reporting β agents auto-pull data, format, and deliver weekly. Bonus: Gemini Enterprise has a visual no-code builder. Build agents once, deploy across your whole team. ποΈ The unlock: people who set this up NOW have a 6-month head start. These systems compound. Every workflow you build today is one less you'll set up later. AI SEO isn't asking questions anymore. It's building agents. π― Want the SOP? DM me. π¬
Watch video
- 26
@Marie_Haynes Β·
This week we saw so many things get set up as we transition to a new era of the web - theΒ agentic web. I did something different with my newsletter this week. I asked Gemini to look at the transcripts from my client calls and Search Bar meetings and pull out the actionable topics I have been discussing. Then I used those to write a completely new kind of newsletter. If you don't have time to read the full blog post, here are the important things to know: βSearch is becoming an "Agentic Manager": Search engines are shifting from simply providing information to utilizing agents that actually get things done. If your search clicks are dropping, it's not likely because of bad SEO. It's probably because AI Mode and AI Overviews answering questions directly. βGemini in Chrome is transforming workflows: Deep browser integration now allows users to converse with AI across multiple open tabs. Skills let you save and reuse prompts to automate repetitive tasks directly in your browser. βGoogle's Antigravity is a massive leap forward: This powerful agent manager is proving incredibly effective for building apps and workflows through simple, conversational prompts. This is not a popular opinion, but I like it better than Claude Code and ChatGPT Codex. βUCP and WebMCP offer a competitive edge: Universal Commerce Protocol (UCP) and WebMCP allow AI agents to natively interact with your website's tools and eCommerce functions. Implementing these early will likely give sites a massive advantage as agentic search becomes the norm. βWebsite management is becoming AI-driven: The way we build and optimize sites is fundamentally changing. With new AI-native tools (like EmDash and Shopify's AI toolkit) and concepts like Andrej Karpathy's autoresearch, we are moving toward an era where agents can continually test, learn, and improve websites autonomously. Read the full newsletter here: https://t.co/IN1AMDarlk
- 27
@TopStockAlerts1 Β·
Progress Software announced new agentic AI capabilities for its Sitefinity Generative CMS platform, introducing AI agents that operate directly within content workflows to handle optimization, review, analysis, and SEO tasks. The update is designed to help marketing teams streamline content operations, reduce manual work, and improve digital experience delivery. Key features include customizable AI agents for specific workflows, page-level intelligence that evaluates content and SEO performance, adaptive learning based on user feedback, and a DX Assistant that answers natural-language questions about content effectiveness and SEO gaps. Progress said the technology moves beyond AI-assisted tools by embedding AI directly into the publishing process. The platform also includes safeguards to prevent conflicting recommendations when multiple agents operate simultaneously. $PRGS
- 28
@JulianGoldieSEO Β·
I BUILT A 4-AGENT SEO MACHINE THAT PUBLISHES RANKING CONTENT IN ONE CLICK One site hit 278 clicks per day. The workflow behind it is the part most SEOs are missing. The Results: β Website 1 grew from zero to 278 clicks per day β Website 2 climbed from zero to 74 clicks per day β Website 3 reached 28 clicks per day and is still growing The System: β Hermes Oracle scans trending news and scores topics by interest and virality β A keyword engine pulls Google Search Console queries getting impressions but no clicks β A 13-step SEO skill writes personalized content using my experiments, dashboards and case studies β Four agents quality-check, publish and submit every URL for indexing The Distribution: β One click deploys unique content across multiple WordPress sites β Internal links, external sources and cross-site references are added automatically β The same keyword becomes an edited video with an AI avatar and B-roll The real advantage is not publishing more AI content. It is combining fresh trends, private Search Console data and original case studies before competitors spot the opportunity.
Watch video
Best Tweets by Topic